Transcepta, a procure-to-pay platform, is ramping up its use of artificial intelligence (AI), the company said late last week. In an announcement, the company said its leveraging of AI and predictive analytics technologies can enable smarter procurement decision making. "Artificial intelligence and predictive analytics are transforming business processes and setting the expectation from customers for seamless function and automation," said Transcepta CEO Ray Parsons in a statement. Its AI capabilities can enable invoice validation and processing features, with 99 percent accuracy in predicting data for missing fields on purchase orders or invoices, the firm said. Last month Euler Hermes inked a partnership with Flowcast to integrate AI into the trade credit insurance process in an effort to mitigate risk of B2B trade. The technology has also permeated across B2B FinTech, with companies like Xero, which operates a small business cloud accounting platform, also deploying AI.

In my last blog, I spoke about the magnitude of damage P2P fraud can cause to the organization and the need to address the problem with a different mindset with a more data driven approach. While traditional approaches help in uncovering some gaps, they suffer from some inherent shortcomings as discussed in one of our earlier posts . Some of these shortcomings are high false positives, inability to uncover newer anomalies and recognize patterns in large datasets, not learning from feedback. We have seen significant upside potential through use of data analytics and machine learning in fraud detection.

In my last blog, I spoke about the magnitude of damage P2P fraud can cause to the organization and the need to address the problem with a different mindset with a more data driven approach. As is conceivable, some of these could be due to human errors and others with an intent to deceive. While traditional approaches help in uncovering some gaps, they suffer from some inherent shortcomings as discussed in one of our earlier posts . Some of these shortcomings are high false positives, inability to uncover newer anomalies and recognize patterns in large datasets, not learning from feedback. We have seen significant upside potential through use of data analytics and machine learning in fraud detection.